How the flow of traffic of cars correlates with bike share data

• Look for areas that people do not use cars as much in
• Put more bikes in the areas where there are less people commuting to work in cars
• Relation between motorized vehicles and bikes
• Parking spaces
• Last mile
• Where best to place bike stations

library(knitr)
library(tidyverse)
library(janitor)
library(lubridate) # because we will probably see some dates
library(here) # more easily access files in your project
library(sf) # working with simple features - geospatial
library(tmap)
library(tidycensus)

bikes = (read.csv(here("data_raw", "202309-capitalbikeshare-tripdata.csv"))) |> clean_names()

racks = st_read((here("data_raw", "Public_Bike_Racks.geojson"))) |> clean_names() 

Reading layer `Public_Bike_Racks' from data source `C:\Users\thistljy\Documents\ds241_final\data_raw\Public_Bike_Racks.geojson' using driver `GeoJSON'
Simple feature collection with 3558 features and 2 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -77.1107 ymin: 38.81903 xmax: -76.91907 ymax: 38.98663
Geodetic CRS:  WGS 84

bike_s = bikes |> slice_sample(n = 100 )

census_api_key("9fc5d3792d3a5e922287c3f4e9995118766d50a2")

To install your API key for use in future sessions, run this function with `install = TRUE`.

v2018 = load_variables(2018, "acs5")

df_censcus=get_acs(geography = "tract",
                  variables=c("vehicles"="B08141_001",
                              "population" = "B01001_001",
                              "public_transportion" = "B08006_008"),
                  state="DC",geometry=TRUE,year=2021) 

Getting data from the 2017-2021 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '11' for state 'DC'

plot(df_censcus)

plot(racks)

tmap_mode("view")

tmap mode set to interactive viewing

df_cens=df_censcus %>% 
  select(-moe) %>% 
  pivot_wider(names_from = "variable", 
              values_from = "estimate")|> 
  mutate(pub_pop = public_transportion / population,
         v_pop = vehicles / population)

bike_routes = st_read((here("data_raw", "Signed_Bike_Routes.geojson"))) |> clean_names() 

Reading layer `Signed_Bike_Routes' from data source `C:\Users\thistljy\Documents\ds241_final\data_raw\Signed_Bike_Routes.geojson' using driver `GeoJSON'
Simple feature collection with 1024 features and 37 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: -77.08773 ymin: 38.8404 xmax: -76.92188 ymax: 38.98634
Geodetic CRS:  WGS 84

df_cens_adj = df_cens |> st_transform(4326)
bike_routes = st_as_sf(bike_routes, crs=st_crs(df_cens_adj))

racks = st_as_sf(racks, crs=st_crs(df_cens_adj))
tm_shape(df_cens) +tm_polygons(c("pub_pop", "v_pop"), alpha=.5) + tm_shape(racks) +tm_symbols(size = 0.01, alpha = 0.5) +   
tm_shape(bike_routes) + tm_lines(col="blue",lwd=1,alpha= 1)

Data Sources:

Public Bike Racks: https://opendata.dc.gov/explore?collection=Dataset&query=Public%20Bike%20Racks Bike Share Data: https://capitalbikeshare.com/system-data Signed Bike Routes: https://opendata.dc.gov/explore?collection=Dataset&query=Bike%20Routes